diff --git a/client/public/files/data-template.xlsx b/client/public/files/data-template.xlsx index 7858923e0..a13d6f1bf 100644 Binary files a/client/public/files/data-template.xlsx and b/client/public/files/data-template.xlsx differ diff --git a/data/base_data_importer/data/1.units.csv b/data/base_data_importer/data/1.units.csv index 7e9f42827..fa3638957 100644 --- a/data/base_data_importer/data/1.units.csv +++ b/data/base_data_importer/data/1.units.csv @@ -8,6 +8,5 @@ "62d0ba5b-c20b-4e65-85b9-823ae7cdb541","milimetres per year","mm/yr","millimeters per year","MMYR" "5bc907ac-3aa7-4e0e-b5ae-3865285c55d1","livestock units","LSU","livestock units","LSU" "9c0da38a-6371-4c79-879b-218fc39c4700","milions of cubic meters per year","Mm3/yr","Millions of cubic meters per year","MM3YR" -"4e7e8927-5425-4a29-b052-9a961516b0b2","% of intactness","%intactness","% of intactenss","%intactness" -"1129394b-6baa-41dd-9409-80d808dbc32e","total number of species","species","total number of species","species" +"1129394b-6baa-41dd-9409-80d808dbc32e","score","score","score","score" "43f3bb46-51d5-4c9f-b9a4-3f9a61b52e56","kilograms per year","kg/yr","kilograms per year","KGYR" diff --git a/data/base_data_importer/data/2.indicator.csv b/data/base_data_importer/data/2.indicator.csv index ac7bc68c8..36e01652d 100644 --- a/data/base_data_importer/data/2.indicator.csv +++ b/data/base_data_importer/data/2.indicator.csv @@ -1,14 +1,11 @@ "id","name","shortName","nameCode","description","unitId","metadata" -"936d0a9f-fe48-42b4-9433-63282d4dada5","Deforestation risk","Deforestation risk","DF_LUC_T","The Deforestation Risk indicator quantifies the level of deforestation happening near commodity production areas and relates it to the quantity of commodity sourced. The term 'risk' is used because it accounts for deforestation occurring in close proximity to the sourcing location, indicating a potential connection between sourcing activities and deforestation. This indicator plays a crucial role in assessing the environmental impacts associated with sourcing activities and aids in decision-making processes to mitigate deforestation risks in commodity production areas. Its implementation facilitates responsible and sustainable practices in commodity sourcing.","6970f9b8-eba0-4fee-b6ee-2723ce6604d4","{""name"": ""Deforestation risk"",""short name"": ""Deforestation risk"",""name code"": ""DF_LUC_T"",""indicator type"": ""landscape-level"",""units"": ""ha/yr"",""description"": ""The Deforestation Risk indicator quantifies the level of deforestation happening near commodity production areas and relates it to the quantity of commodity sourced. The term 'risk' is used because it accounts for deforestation occurring in close proximity to the sourcing location, indicating a potential connection between sourcing activities and deforestation. This indicator plays a crucial role in assessing the environmental impacts associated with sourcing activities and aids in decision-making processes to mitigate deforestation risks in commodity production areas. Its implementation facilitates responsible and sustainable practices in commodity sourcing."",""license"": ""CC BY 4.0"",""geographic coverage"": ""Global coverage (excluding Antarctica and other Arctic islands)."",""citation"": [ ""Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53. "", ""Elise Mazur, Michelle Sims, Elizabeth Goldman, Martina Schneider, Marco Daldoss Pirri, Craig R. Beatty, Fred Stolle. 2023. Natural Lands Map v1.0"", ""Potapov, P., M.C. Hansen, L. Laestadius, S. Turubanova, A. Yaroshenko,C. Thies, W. Smith, I. Zhuravleva, A. Komarova, S. Minnemeyer, and E. Esipova. 2017. “The last frontiers of wilderness: Tracking loss of intact forest landscapes from 2000 to 2013.” Science Advances 3 (1). https://www.science.org/doi/10.1126/sciadv.1600821"", ""Turubanova S., Potapov P., Tyukavina, A., and Hansen M. (2018) Ongoing primary forest loss in Brazil, Democratic Republic of the Congo, and Indonesia. Environmental Research Letters https://doi.org/10.1088/1748-9326/aacd1c"", ""Harris, N.L., E.D. Goldman, and J. Richter. In preparation. “Spatial Database of Planted Trees Version 2.0.” Technical Note. Washington, DC: World Resources Institute."", ""Hansen, M.C., P.V. Potapov, A.H. Pickens, A. Tyukavina, A. Hernandez-Serna, V. Zalles, S. Turubanova, I. Kommareddy, S.V. Stehman, and X-P Song. 2022. “Global land use extent and dispersion within natural land cover using Landsat data.” Environmental Research Letters 17 (3). https://doi.org/10.1088/1748-9326/ac46ec"", ""Emilio Chuvieco, Florent Mouillot, Guido R. van der Werf, Jesús San Miguel, Mihai Tanase, Nikos Koutsias, Mariano García, Marta Yebra, Marc Padilla, Ioannis Gitas, Angelika Heil, Todd J. Hawbaker, Louis Giglio. 2018. ”Historical background and current developments for mapping burned area from satellite Earth observation”. Remote Sensing of Environtem. Volume 225 (May 2019) Pages 45-64 https://doi.org/10.1016/j.rse.2019.02.013""],""source"": [ ""Hansen/UMD/Google/USGS/NASA"", ""Mazur/Potapov/Turubanova/Richter/Chuvieco""],""frequency of updates"": ""Annual"",""date of content"": ""2001-2022"",""resolution"": ""30x30 meters""}" -"157b5f22-916b-4981-84c7-f6607ec65445","Climate risk from land use change","Climate risk","GHG_LUC_T","The Climate Risk from Land Use Change indicator quantifies the greenhouse gas emissions associated with deforestation near agricultural production areas. It considers the carbon stocks in vegetation and soil, estimating emissions based on changes in these stocks due to deforestation. By assessing the potential contribution of deforestation to greenhouse gas emissions, this indicator plays a crucial role in understanding and mitigating the climate risks associated with commodity sourcing.","a0e8110c-fbde-4c8c-ac19-f0f69078b96b","{""name"": ""Climate risk from land use change"",""short name"": ""Climate risk"",""name code"": ""GHG_LUC_T"",""indicator type"": ""landscape-level"",""units"": ""tCO2eq/yr"",""description"": ""The Climate Risk from Land Use Change indicator quantifies the greenhouse gas emissions associated with deforestation near agricultural production areas. It considers the carbon stocks in vegetation and soil, estimating emissions based on changes in these stocks due to deforestation. By assessing the potential contribution of deforestation to greenhouse gas emissions, this indicator plays a crucial role in understanding and mitigating the climate risks associated with commodity sourcing."",""license"": ""CC BY 4.0"",""geographic coverage"": ""Global coverage."",""citation"": [ ""Noon, M.L., Goldstein, A., Ledezma, J.C. et al. Mapping the irrecoverable carbon in Earth’s ecosystems. Nat Sustain 5, 37–46 (2022). https://doi.org/10.1038/s41893-021-00803-6"", ""Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53. "", ""Elise Mazur, Michelle Sims, Elizabeth Goldman, Martina Schneider, Marco Daldoss Pirri, Craig R. Beatty, Fred Stolle. 2023. Natural Lands Map v1.0"", ""Potapov, P., M.C. Hansen, L. Laestadius, S. Turubanova, A. Yaroshenko,C. Thies, W. Smith, I. Zhuravleva, A. Komarova, S. Minnemeyer, and E. Esipova. 2017. “The last frontiers of wilderness: Tracking loss of intact forest landscapes from 2000 to 2013.” Science Advances 3 (1). https://www.science.org/doi/10.1126/sciadv.1600821"", ""Turubanova S., Potapov P., Tyukavina, A., and Hansen M. (2018) Ongoing primary forest loss in Brazil, Democratic Republic of the Congo, and Indonesia. Environmental Research Letters https://doi.org/10.1088/1748-9326/aacd1c"", ""Harris, N.L., E.D. Goldman, and J. Richter. In preparation. “Spatial Database of Planted Trees Version 2.0.” Technical Note. Washington, DC: World Resources Institute."", ""Hansen, M.C., P.V. Potapov, A.H. Pickens, A. Tyukavina, A. Hernandez-Serna, V. Zalles, S. Turubanova, I. Kommareddy, S.V. Stehman, and X-P Song. 2022. “Global land use extent and dispersion within natural land cover using Landsat data.” Environmental Research Letters 17 (3). https://doi.org/10.1088/1748-9326/ac46ec"", ""Emilio Chuvieco, Florent Mouillot, Guido R. van der Werf, Jesús San Miguel, Mihai Tanase, Nikos Koutsias, Mariano García, Marta Yebra, Marc Padilla, Ioannis Gitas, Angelika Heil, Todd J. Hawbaker, Louis Giglio. 2018. ”Historical background and current developments for mapping burned area from satellite Earth observation”. Remote Sensing of Environtem. Volume 225 (May 2019) Pages 45-64 https://doi.org/10.1016/j.rse.2019.02.013""],""source"": [ ""Noon/Mazur/Potapov/Turubanova/Richter/Chuvieco"", ""Hansen/UMD/Google/USGS/NASA""],""frequency of updates"": ""Annual"",""date of content"": ""2001-2021"",""resolution"": ""30x30 meters""}" -"3a718205-10c1-4e11-81b3-cb8965e378c7","Species - Biodiversity loss from land use change","Species loss","BL_LUC_S","The total number of mapped species whose area of habitat is associated with forest loss near agricultural production areas. Rarity-weighted richness serves as a measure that can be used to modify indicators. It combines species richness with the endemism of the species occurring in a given grid cell to reflect the importance of the habitat being lost for the species occurring in that location.","1129394b-6baa-41dd-9409-80d808dbc32e","{""name"": ""Species - Biodiversity loss from land use change"",""short name"": ""Species loss"",""name code"": ""BL_LUC_S"",""indicator type"": ""landscape-level"",""units"":""species"",""description"": ""The total number of mapped species whose area of habitat is associated with forest loss near agricultural production areas. Rarity-weighted richness serves as a measure that can be used to modify indicators. It combines species richness with the endemism of the species occurring in a given grid cell to reflect the importance of the habitat being lost for the species occurring in that location."",""license"": """",""geographic coverage"": ""Global coverage."",""citation"": [ ""Rarity-weighted richness: a simple and reliable alternative to integer programming and heuristic algorithms for minimum set and maximum coverage problems in conservation planning, PLoS ONE, 10 (2015), pp. 1-7, https://10.1371/journal.pone.0119905""],""source"":[""""],""frequency of updates"": """",""date of content"": """",""resolution"": """"}" -"e2392fb1-78cd-4c2f-997d-71872a2172dc","Ecosystems - Biodiversity loss from land use change","Ecosystems loss","BL_LUC_E","The change in ecosystem quality and structure associated with the sourcing of a material. This indicator expresses the average degree of intactness due to factors and indices such as habitat loss or the change in ecosystem structure as a result of deforestation.","4e7e8927-5425-4a29-b052-9a961516b0b2","{""name"": ""Ecosystems - Biodiversity loss from land use change"",""short name"": ""Ecosystems loss"",""name code"": ""BL_LUC_E"",""indicator type"": ""landscape-level"",""units"":""% of intactness"",""description"": ""The change in ecosystem quality and structure associated with the sourcing of a material. This indicator expresses the average degree of intactness due to factors and indices such as habitat loss or the change in ecosystem structure as a result of deforestation."",""license"": """",""geographic coverage"": ""Global coverage."",""citation"": [ ""Beyer, H.L., Venter, O., Grantham, H.S. and Watson, J.E., 2020. Substantial losses in ecoregion intactness highlight urgency of globally coordinated action. Conservation Letters, 13(2), p.e12692. https://doi.org/10.1111/conl.12692""],""source"":[""Beyer 2020""],""frequency of updates"": """",""date of content"": """",""resolution"": """"}" -"9c2124c7-5df0-40d5-962e-d35480d48cd3","Water use","Water use","UWU_T","The Water Use indicator estimates the amount of surface or groundwater resources consumed in the production of a commodity. It focuses on the blue water footprint, which represents water sourced from surface or groundwater and includes evaporation, product incorporation, and water transfers. By quantifying water use, this indicator provides insights into the direct and indirect appropriation of freshwater resources associated with the commodity's production, aiding in responsible water resource management.","9c0da38a-6371-4c79-879b-218fc39c4700","{""name"": ""Water use"",""short name"": ""Water use"",""name code"": ""UWU_T"",""indicator type"": ""farm-level"",""units"": ""Mm3/yr"",""description"": ""The Water Use indicator estimates the amount of surface or groundwater resources consumed in the production of a commodity. It focuses on the blue water footprint, which represents water sourced from surface or groundwater and includes evaporation, product incorporation, and water transfers. By quantifying water use, this indicator provides insights into the direct and indirect appropriation of freshwater resources associated with the commodity's production, aiding in responsible water resource management."",""license"": ""CC BY 4.0"",""geographic coverage"": ""Global coverage."",""citation"": [""Mekonnen, M.M. & Hoekstra, A.Y. (2011) The green, blue and grey water footprint of crops and derived crop products, Hydrology and Earth System Sciences, 15(5): 1577-1600."",""Mekonnen, M.M. & Hoekstra, A.Y. (2012) A global assessment of the water footprint of farm animal products, Ecosystems, 15(3): 401–415.""],""source"": [ ""Mekonnen 2011"", ""Mekonnen 2012""],""frequency of updates"": """",""date of content"": ""1996-2005"",""resolution"": """"}" -"ffdd6f19-6737-4a10-9d36-5243d3f14b45","Unsustainable water use","Unsustainable water use","UWUSR_T","The Unsustainable Water Use indicator assesses the impact of water usage in regions experiencing water stress. It focuses on areas where the commodity's raw material is grown and calculates the water use associated with it. Using the baseline water stress data, which measures the ratio of water withdrawals to available supplies, the indicator identifies high-risk areas with a baseline water stress exceeding 0.4. This threshold is used to indicate a high risk of unsustainable water use. By quantifying unsustainable water use, this indicator contributes to the understanding and mitigation of the environmental risks related to water scarcity in commodity production.","9c0da38a-6371-4c79-879b-218fc39c4700","{""name"": ""Unsustainable water use"",""short name"": ""Unsustainable water use"",""name code"": ""UWUSR_T"",""indicator type"": ""farm-level"",""units"": ""Mm3/yr"",""description"": ""The Unsustainable Water Use indicator assesses the impact of water usage in regions experiencing water stress. It focuses on areas where the commodity's raw material is grown and calculates the water use associated with it. Using the baseline water stress data, which measures the ratio of water withdrawals to available supplies, the indicator identifies high-risk areas with a baseline water stress exceeding 0.4. This threshold is used to indicate a high risk of unsustainable water use. By quantifying unsustainable water use, this indicator contributes to the understanding and mitigation of the environmental risks related to water scarcity in commodity production."",""license"": ""CC BY 4.0"",""geographic coverage"": ""Global coverage."",""citation"": [ ""Kuzma, S., M.F.P. Bierkens, S.Lakshman, T. Luo, L. Saccoccia, E. H. Sutanudjaja, and R. Van Beek. 2023. “Aqueduct 4.0: Updated decision-relevant global water risk indicators.” Technical Note. Washington, DC: World Resources Institute. Available online at: doi.org/10.46830/ writn.23.00061."", ""Mekonnen, M.M. & Hoekstra, A.Y. (2011) The green, blue and grey water footprint of crops and derived crop products, Hydrology and Earth System Sciences, 15(5): 1577-1600."", ""Mekonnen, M.M. & Hoekstra, A.Y. (2012) A global assessment of the water footprint of farm animal products, Ecosystems, 15(3): 401–415.""],""source"": [ ""Aqueduct 4.0 2019"", ""Mekonnen 2011"", ""Mekonnen 2012""],""frequency of updates"": """",""date of content"": ""2019"",""resolution"": """"}" -"5c595ac7-f144-485f-9f32-601f6faae9fe","Land use","Land use","LI","The Land Use indicator quantifies the total land area necessary to produce a given quantity of a sourced commodity. It utilizes a land use impact factor derived from crop yield and scaled by the production volume. The calculation of the land use associated with commodity production provides an assessment of the environmental impact of land utilization and helps in the decision making process to promote sustainable land management practices.","6970f9b8-eba0-4fee-b6ee-2723ce6604d4","{""name"": ""Land use"",""short name"": ""Land use"",""name code"": ""LI"",""indicator type"": ""farm-level"",""units"": ""ha/yr"",""description"": ""The Land Use indicator quantifies the total land area necessary to produce a given quantity of a sourced commodity. It utilizes a land use impact factor derived from crop yield and scaled by the production volume. The calculation of the land use associated with commodity production provides an assessment of the environmental impact of land utilization and helps in the decision making process to promote sustainable land management practices."",""license"": ""CC BY 4.0"",""geographic coverage"": ""Global coverage."",""citation"": [ ""International Food Policy Research Institute, 2019, “Global Spatially-Disaggregated Crop Production Statistics Data for 2010 Version 2.0”, https://doi.org/10.7910/DVN/PRFF8V, Harvard Dataverse, V4"", ""Yu, Q., You, L., Wood-Sichra, U., Ru, Y., et. al. 2020. A cultivated planet in 2010: 2. the global gridded agricultural production maps, Earth Syst. Sci. Data Discuss. https://doi.org/10.5194/essd-2020-11"", ""Gilbert, M., Nicolas, G., Cinardi, G., Van Boeckel, T.P., et. al. 2018. Global distribution data for cattle, buffaloes, horses, sheep, goats, pigs, chickens and ducks in 2010. Scientific data, 5(1), pp.1-11. https://doi.org/10.1038/sdata.2018.227""],""source"": [ ""Mapspam 2010"", ""LGW3 2010""],""frequency of updates"": """",""date of content"": ""2010"",""resolution"": ""10x10 kilometers""}" -"1994e7bf-5442-4061-ba48-6320574263ad","Greenhouse gas emissions","Greenhouse gas emissions","GHG","The Greenhouse Gas Emissions (GHG) from Farm Production indicator quantifies the emissions within the farm-gate arising from food production, including CO2, CH4 and N2O emissions from various activities such as crop residue burning, irrigation, fertilizer production and manure management. Land use conversion emissions are not included since they occur outside the farm-gate. Emissions are mapped to production locations based on crop and livestock data. To calculate the farm-level GHG impact factor for a commodity sourced from a specific area, emissions are weighted by the production in each location.","a0e8110c-fbde-4c8c-ac19-f0f69078b96b","{""name"": ""Greenhouse gas emissions"",""short name"": ""Greenhouse gas emissions"",""name code"": ""GHG"",""indicator type"": ""farm-level"",""units"": ""tCO2eq/yr"",""description"": ""The Greenhouse Gas Emissions (GHG) from Farm Production indicator quantifies the emissions within the farm-gate arising from food production, including CO2, CH4 and N2O emissions from various activities such as crop residue burning, irrigation, fertilizer production and manure management. Land use conversion emissions are not included since they occur outside the farm-gate. Emissions are mapped to production locations based on crop and livestock data. To calculate the farm-level GHG impact factor for a commodity sourced from a specific area, emissions are weighted by the production in each location."",""license"": ""CC BY 4.0"",""geographic coverage"": ""Global coverage."",""citation"": [ ""Benjamin S. Halpern, Melanie Frazier, Juliette Verstaen, Paul-Eric Rayner, Gage Clawson, Julia L. Blanchard, Richard S. Cottrell, Halley E. Froehlich, Jessica A. Gephart, Nis S. Jacobsen, Caitlin D. Kuempel, Peter B. McIntyre, Marc Metian, Daniel Moran, Kirsty L. Nash, Johannes Többen, David R. Williams. (2021) The environmental footprint of global food production.Scientific Reports.https://doi.org/10.1038/s41893-022-00965-x""],""source"": [ ""Halpern et al 2021""],""frequency of updates"": """",""date of content"": """",""resolution"": """"}" -"776f101e-b574-4e36-8410-81e573afab83","Satelligence deforestation","Satelligence deforestation","SAT_DF","Forest loss occurring near material production areas. Deforestation is either calculated using Satelligence models trained to identify transitions from forest to non-forest states in satellite imagery, or using global tree cover loss data from Global Forest Watch.","6970f9b8-eba0-4fee-b6ee-2723ce6604d4","{""name"": ""Satelligence deforestation"",""short name"": ""Satelligence deforestation"",""name code"": ""SAT_DF"",""indicator type"": ""landscape-level"",""units"":""ha/yr"",""description"": ""Forest loss occurring near material production areas. Deforestation is either calculated using Satelligence models trained to identify transitions from forest to non-forest states in satellite imagery, or using global tree cover loss data from Global Forest Watch."",""license"": ""CC BY 4.0"",""geographic coverage"": ""Global coverage (excluding Antarctica and other Arctic islands)."",""citation"": [""Satelligence, 2022. Forest Change Bulletins. https://satelligence.com/technology"",""Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, et al. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (6160): 850–53. https://doi.org/10.1126/science.1244693. ""],""source"":[""Satelligence 2022"",""Hansen 2001-2021""],""frequency of updates"": ""Annual"",""date of content"": ""2001-2021"",""resolution"": ""30x30 meters""}" -"169c0638-9cf5-4048-b448-2652dc56ee6a","Satelligence deforestation risk","Satelligence deforestation risk","SAT_DF_R","Forest loss occurring near material production areas. Deforestation is either calculated using Satelligence models trained to identify transitions from forest to non-forest states in satellite imagery, or using global tree cover loss data from Global Forest Watch.","6970f9b8-eba0-4fee-b6ee-2723ce6604d4","{""name"": ""Satelligence deforestation risk"",""short name"": ""Satelligence deforestation risk"",""name code"": ""SAT_DF_R"",""indicator type"": ""landscape-level"",""units"":""ha/yr"",""description"": ""Forest loss occurring near material production areas. Deforestation is either calculated using Satelligence models trained to identify transitions from forest to non-forest states in satellite imagery, or using global tree cover loss data from Global Forest Watch."",""license"": ""CC BY 4.0"",""geographic coverage"": ""Global coverage (excluding Antarctica and other Arctic islands)."",""citation"": [""Satelligence, 2022. Forest Change Bulletins. https://satelligence.com/technology"",""Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, et al. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (6160): 850–53. https://doi.org/10.1126/science.1244693. ""],""source"":[""Satelligence 2022"",""Hansen 2001-2021""],""frequency of updates"": ""Annual"",""date of content"": ""2001-2021"",""resolution"": ""30x30 meters""}" -"d5f945c9-8636-45a2-a7c9-67a1dc8e687a","Water quality","Water quality","WQ","The Water Quality indicator estimates the annual average water volume required to assimilate the nutrient load. It focuses on the grey water footprint of a commodity, which indicates the volume of freshwater required to absorb the pollutants present, based on prevailing ambient water quality standards. In the context of crop production, the grey water footprint serves as an indicator of the volume of freshwater pollution. By quantifying water quality, this indicator provides insights into the amount of water necessary to assimilate the nutrients that ultimately reach either ground- or surface water.","9c0da38a-6371-4c79-879b-218fc39c4700","{""name"": ""Water quality"",""short name"": ""Water quality"",""name code"": ""WQ"",""indicator type"": ""farm-level"",""units"": ""Mm3/yr"",""description"": ""The Water Quality indicator estimates the annual average water volume required to assimilate the nutrient load. It focuses on the grey water footprint of a commodity, which indicates the volume of freshwater required to absorb the pollutants present, based on prevailing ambient water quality standards. In the context of crop production, the grey water footprint serves as an indicator of the volume of freshwater pollution. By quantifying water quality, this indicator provides insights into the amount of water necessary to assimilate the nutrients that ultimately reach either ground- or surface water."",""license"": ""CC BY 4.0"",""geographic coverage"": ""Global coverage."",""citation"": [""Mekonnen, M.M. & Hoekstra, A.Y. (2011) The green, blue and grey water footprint of crops and derived crop products, Hydrology and Earth System Sciences, 15(5): 1577-1600."", ""Mekonnen, M.M. & Hoekstra, A.Y. (2012) A global assessment of the water footprint of farm animal products, Ecosystems, 15(3): 401–415.""],""source"": [ ""Mekonnen 2011"", ""Mekonnen 2012""],""frequency of updates"": """",""date of content"": ""1996-2005"",""resolution"": """"}" -"a39394be-ad57-41bc-9c2c-be0949ec6193","Nutrient assimilation capacity","Nutrient assimilation capacity","NAC","The Nutrient Assimilation Capacity indicator aims to assess and quantify the maximum amount of nutrient load that will attain the desired instream nutrient concentration.","9c0da38a-6371-4c79-879b-218fc39c4700","{""name"": ""Nutrient assimilation capacity"",""short name"": ""Nutrient assimilation capacity"",""name code"": ""NAC"",""indicator type"": ""farm-level"",""units"": ""Mm3/yr"",""description"": ""The Nutrient Assimilation Capacity indicator aims to assess and quantify the maximum amount of nutrient load that will attain the desired instream nutrient concentration."",""license"": ""CC BY 4.0"",""geographic coverage"": ""Global coverage."",""citation"": [ ""McDowell, R. W., A. Noble, P. Pletnyakov, B. E. Haggard and L. M. Mosley, 2020. Global Mapping of Freshwater Nutrient Enrichment and Periphyton Growth Potential. Scientific Reports.https://doi.org/10.1038/s41598-020-60279-w"", ""Benjamin S. Halpern, Melanie Frazier, Juliette Verstaen, Paul-Eric Rayner, Gage Clawson, Julia L. Blanchard, Richard S. Cottrell, Halley E. Froehlich, Jessica A. Gephart, Nis S. Jacobsen, Caitlin D. Kuempel, Peter B. McIntyre, Marc Metian, Daniel Moran, Kirsty L. Nash, Johannes Többen, David R. Williams. (2021) The environmental footprint of global food production.Scientific Reports.https://doi.org/10.1038/s41893-022-00965-x"" ,""Mekonnen, M.M. & Hoekstra, A.Y. (2011) The green, blue and grey water footprint of crops and derived crop products, Hydrology and Earth System Sciences, 15(5): 1577-1600."",""Mekonnen, M.M. & Hoekstra, A.Y. (2012) A global assessment of the water footprint of farm animal products, Ecosystems, 15(3): 401–415.""],""source"": [ ""McDowell et al 2020"", ""Mekonnen 2011"", ""Mekonnen 2012""],""frequency of updates"": """",""date of content"": """",""resolution"": """"}" -"5c133ba4-da24-46db-9c6c-ece7520f01b0","Natural ecosystems conversion risk","Natural ecosystems conversion risk","NECR","The Natural Ecosystems Conversion Risk indicator quantifies the conversion of natural ecosystems occurring near commodity production areas and allocates the risk to the quantity of commodity sourced. IT uses a statistical land use change method to calculate deforestation within a buffered region around the sourcing location. Remote sensing imagery and machine learning algorithms are used to identify forest loss, and the annual rate of deforestation is weighted by commodity production to account for areas where the commodity is grown. The indicator provides insight into the risk of sourcing contributing to deforestation, distinguishing between natural forest loss and loss from plantations. Human land use is factored in by combining crop data, pasture and plantation areas.","6970f9b8-eba0-4fee-b6ee-2723ce6604d4","{""name"": ""Natural ecosystems conversion risk"",""short name"": ""Natural ecosystems conversion risk"",""name code"": ""NECR"",""indicator type"": ""landscape-level"",""units"": ""ha/yr"",""description"": ""The Natural Ecosystems Conversion Risk indicator quantifies the conversion of natural ecosystems occurring near commodity production areas and allocates the risk to the quantity of commodity sourced. It uses a statistical land use change method to calculate deforestation within a buffered region around the sourcing location. Remote sensing imagery and machine learning algorithms are used to identify forest loss, and the annual rate of deforestation is weighted by commodity production to account for areas where the commodity is grown. The indicator provides insight into the risk of sourcing contributing to deforestation, distinguishing between natural forest loss and loss from plantations. Human land use is factored in by combining crop data, pasture and plantation areas."",""license"": ""CC BY 4.0"",""geographic coverage"": ""Global coverage."",""citation"": [ ""Karra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021."", ""Elise Mazur, Michelle Sims, Elizabeth Goldman, Martina Schneider, Marco Daldoss Pirri, Craig R. Beatty, Fred Stolle. 2023. Natural Lands Map v1.0""],""source"": [ ""Esri/Sentinel-2 10m land use/land cover"", ""SBTN/WRI/Systemiq/WWF""],""frequency of updates"": ""Annual"",""date of content"": ""2023"",""resolution"": ""10x10 kilometers""}" +"936d0a9f-fe48-42b4-9433-63282d4dada5","Deforestation footprint (sLUC)","Deforestation footprint (sLUC)","DF_LUC_T","The deforestation footprint (sLUC) indicator quantifies the annual average area of deforestation within a 50km radius attributable to the raw material sourced.","6970f9b8-eba0-4fee-b6ee-2723ce6604d4","{ ""name"": ""Deforestation footprint (sLUC)"", ""short name"": ""Deforestation footprint (sLUC)"", ""name code"": ""DF_LUC_T"", ""indicator type"": ""landscape-level"", ""impact type"": ""Natural ecosystem conversion"", ""units"": ""ha/yr"", ""description"": ""The deforestation footprint (sLUC) indicator quantifies the annual average area of deforestation within a 50km radius attributable to the raw material sourced."", ""interpretation"":""Deforestation footprint estimates the area of deforestation occurring within a 50km radius that is attributable to the quantity of raw material sourced using a statistical land use change (sLUC) approach. The indicator assumes that deforestation is driven by demand for land area."", ""license"": ""CC BY 4.0"", ""geographic coverage"": ""Global coverage (excluding Antarctica and other Arctic islands)."", ""citation"": [ ""Chuvieco, Emilio, Joshua Lizundia-Loiola, Maria Lucrecia Pettinari, Ruben Ramo, Marc Padilla, Kevin Tansey, Florent Mouillot, et al. 2018. ‘Generation and Analysis of a New Global Burned Area Product Based on MODIS 250 m Reflectance Bands and Thermal Anomalies’. Earth System Science Data 10 (4): 2015–31. https://doi.org/10.5194/essd-10-2015-2018."", ""Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, et al. 2013. ‘High-Resolution Global Maps of 21st-Century Forest Cover Change’. Science 342 (6160): 850–53. https://doi.org/10.1126/science.1244693"", ""Mazur, Elise, Michelle Sims, Elizabeth Goldman, Martina Schneider, Fred Stolle, Marco Daldoss Pirri, and Craig Beatty. 2023. ‘SBTN Natural Lands Map: Technical Documentation’. SBTN. https://sciencebasedtargetsnetwork.org/wp-content/uploads/2023/05/Technical-Guidance-2023-Step3-Land-v0.3-Natural-Lands-Map.pdf."", ""Potapov, Peter, Matthew C. Hansen, Lars Laestadius, Svetlana Turubanova, Alexey Yaroshenko, Christoph Thies, Wynet Smith, et al. 2017. ‘The Last Frontiers of Wilderness: Tracking Loss of Intact Forest Landscapes from 2000 to 2013’. Science Advances 3 (1): e1600821. https://doi.org/10.1126/sciadv.1600821."", ""Potapov, Peter, Matthew C. Hansen, Amy Pickens, Andres Hernandez-Serna, Alexandra Tyukavina, Svetlana Turubanova, Viviana Zalles, et al. 2022. ‘The Global 2000-2020 Land Cover and Land Use Change Dataset Derived From the Landsat Archive: First Results’. Frontiers in Remote Sensing 3 (April): 856903. https://doi.org/10.3389/frsen.2022.856903. "", ""Turubanova, Svetlana, Peter V Potapov, Alexandra Tyukavina, and Matthew C Hansen. 2018. ‘Ongoing Primary Forest Loss in Brazil, Democratic Republic of the Congo, and Indonesia’. Environmental Research Letters 13 (7): 074028. https://doi.org/10.1088/1748-9326/aacd1c."" ], ""source"": [ ""Hansen/UMD/Google/USGS/NASA"", ""Mazur/Potapov/Turubanova/Chuvieco"" ], ""frequency of updates"": ""Annual"", ""date of content"": ""2001-2022"", ""resolution"": ""100x100 meters"" }" +"157b5f22-916b-4981-84c7-f6607ec65445","GHG emissions from deforestation (sLUC)","GHGs (deforestation, sLUC)","GHG_LUC_T","The GHG emissions from deforestation (sLUC) indicator quantifies the annual average emissions of greenhouse gas (GHG) associated with deforestation within a 50km radius attributable to the raw material sourced.","a0e8110c-fbde-4c8c-ac19-f0f69078b96b","{ ""name"": ""GHG emissions from deforestation (sLUC)"", ""short name"": ""GHGs (deforestation, sLUC)"", ""name code"": ""GHG_LUC_T"", ""indicator type"": ""landscape-level"", ""impact type"": ""Climate"", ""units"": ""tCO2eq/yr"", ""description"": ""The GHG emissions from deforestation (sLUC) indicator quantifies the annual average emissions of greenhouse gas (GHG) associated with deforestation within a 50km radius attributable to the raw material sourced."", ""interpretation"": ""Provides an estimate of the annual average greenhouse gas emissions arising from deforestation events occurring since 2002, within a 50km proximity of where a raw material was sourced and attributable to that raw material. Emissions are calculated from the deforestation rates and the vulnerable carbon, the amount of biomass and soil carbon that would be lost in a land use change event typical for the location."", ""license"": ""CC BY 4.0"", ""geographic coverage"": ""Global coverage."", ""citation"": [ ""Noon, Monica L., Allie Goldstein, Juan Carlos Ledezma, Patrick R. Roehrdanz, Susan C. Cook-Patton, Seth A. Spawn-Lee, Timothy Maxwell Wright, et al. 2021. ‘Mapping the Irrecoverable Carbon in Earth’s Ecosystems’. Nature Sustainability 5 (1): 37–46. https://doi.org/10.1038/s41893-021-00803-6."", ""ESA. 2017. ‘Land Cover CCI Product User Guide Version 2. Technical Report’. maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf."", ""Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, et al. 2013. ‘High-Resolution Global Maps of 21st-Century Forest Cover Change’. Science 342 (6160): 850–53. https://doi.org/10.1126/science.1244693."" ], ""source"": [ ""Noon/Mazur/Potapov/Turubanova/Chuvieco"", ""Hansen/UMD/Google/USGS/NASA"" ], ""frequency of updates"": ""Annual"", ""date of content"": ""2001-2021"", ""resolution"": ""100x100 meters"" }" +"3a718205-10c1-4e11-81b3-cb8965e378c7","Biodiversity importance of natural ecosystems converted to cropland (FLII)","Forest landscape integrity loss","BL_LUC_S","The biodiversity importance of natural ecosystems converted to cropland (FLII) indicators quantifies the average forest landscape integrity score of natural ecosystems that have been converted to cropland within a 50km radius attributable to the raw material sourced.","1129394b-6baa-41dd-9409-80d808dbc32e","{ ""name"": ""Biodiversity importance of natural ecosystems converted to cropland (FLII)"", ""short name"": ""Forest landscape integrity loss"", ""name code"": ""BL_LUC_S"", ""indicator type"": ""landscape-level"", ""impact type"": ""Biodiversity"", ""units"": ""score"", ""description"": ""The biodiversity importance of natural ecosystems converted to cropland (FLII) indicators quantifies the average forest landscape integrity score of natural ecosystems that have been converted to cropland within a 50km radius attributable to the raw material sourced."", ""interpretation"": ""Quantifies the biodiversity importance of land use change events, specifically here the net expansion of cropland into natural ecosystems. Biodiversity importance is measured here as the Forest Landscape Integrity Index, which represents how ecological intact forest ecosystems are. So higher values indicate that more intact forest landscapes, those with greater ecological importance, have been lost. "", ""license"": """", ""geographic coverage"": ""Global coverage."", ""citation"": [ ""Grantham, H. S., A. Duncan, T. D. Evans, K. R. Jones, H. L. Beyer, R. Schuster, J. Walston, et al. 2020. ‘Anthropogenic Modification of Forests Means Only 40% of Remaining Forests Have High Ecosystem Integrity’. Nature Communications 11 (1): 5978. https://doi.org/10.1038/s41467-020-19493-3."" ], ""source"": [ ""Grantham et al. 2020"" ], ""frequency of updates"": """", ""date of content"": ""2020"", ""resolution"": ""100x100 meters"" }" +"9c2124c7-5df0-40d5-962e-d35480d48cd3","Surface or groundwater use","Water use","UWU_T","The surface or groundwater use indicator estimates the volume of surface or groundwater that is consumed in the production of the raw material sourced.","9c0da38a-6371-4c79-879b-218fc39c4700","{ ""name"": ""Surface or groundwater use"", ""short name"": ""Water use"", ""name code"": ""UWU_T"", ""indicator type"": ""farm-level"", ""impact type"": ""Water quantity"", ""units"": ""Mm3/yr"", ""description"": ""The surface or groundwater use indicator estimates the volume of surface or groundwater that is consumed in the production of the raw material sourced."", ""interpretation"": ""The water use indicator describes the average volume of water consumed in the production of raw materials in a given country context. It is intended to align with the Science Based Targets Network (SBTN) water quantity target indicator (Science Based Targets Network 2023b)."", ""license"": ""CC BY 4.0"", ""geographic coverage"": ""Global coverage."", ""citation"": [ ""Mekonnen, M.M. & Hoekstra, A.Y. (2011) The green, blue and grey water footprint of crops and derived crop products, Hydrology and Earth System Sciences, 15(5): 1577-1600."", ""Mekonnen, M.M. & Hoekstra, A.Y. (2012) A global assessment of the water footprint of farm animal products, Ecosystems, 15(3): 401–415."" ], ""source"": [ ""Mekonnen 2011"", ""Mekonnen 2012"" ], ""frequency of updates"": """", ""date of content"": ""1996-2005"", ""resolution"": """" }" +"ffdd6f19-6737-4a10-9d36-5243d3f14b45","Excess surface or groundwater use","Unsustainable water use","UWUSR_T","The excess surface or groundwater use indicator calculates the volume by which the water consumption associated with the production of the raw material sourced must be decreased to reduce pressure on nature.","9c0da38a-6371-4c79-879b-218fc39c4700","{ ""name"": ""Excess surface or groundwater use"", ""short name"": ""Unsustainable water use"", ""name code"": ""UWUSR_T"", ""indicator type"": ""farm-level"", ""impact type"": ""Water quantity"", ""units"": ""Mm3/yr"", ""description"": ""The excess surface or groundwater use indicator calculates the volume by which the water consumption associated with the production of the raw material sourced must be decreased to reduce pressure on nature."", ""interpretation"": ""The unsustainable water use indicator shows the amount by which water use would need to be reduced in order to reduce pressure on local watersheds and return them to a maximum allowable level of basin-wide withdrawals, according to the Science Based Targets Network (SBTN) water quantity target approach (Science Based Targets Network 2023b). Unsustainable water use is measured as a proportion of total water use."", ""license"": ""CC BY 4.0"", ""geographic coverage"": ""Global coverage."", ""citation"": [ ""Kuzma, S., M.F.P. Bierkens, S.Lakshman, T. Luo, L. Saccoccia, E. H. Sutanudjaja, and R. Van Beek. 2023. “Aqueduct 4.0: Updated decision-relevant global water risk indicators.” Technical Note. Washington, DC: World Resources Institute. Available online at: doi.org/10.46830/ writn.23.00061."", ""Mekonnen, M.M. & Hoekstra, A.Y. (2011) The green, blue and grey water footprint of crops and derived crop products, Hydrology and Earth System Sciences, 15(5): 1577-1600."", ""Mekonnen, M.M. & Hoekstra, A.Y. (2012) A global assessment of the water footprint of farm animal products, Ecosystems, 15(3): 401–415."" ], ""source"": [ ""Aqueduct 4.0 2019"", ""Mekonnen 2011"", ""Mekonnen 2012"" ], ""frequency of updates"": """", ""date of content"": ""2019"", ""resolution"": """" }" +"5c595ac7-f144-485f-9f32-601f6faae9fe","Land use footprint for production","Land footprint","LI","The land use footprint for production indicator quantifies the total land area required to produce the raw material sourced.","6970f9b8-eba0-4fee-b6ee-2723ce6604d4","{ ""name"": ""Land use footprint for production"", ""short name"": ""Land footprint"", ""name code"": ""LI"", ""indicator type"": ""farm-level"", ""impact type"": ""Land use"", ""units"": ""ha/yr"", ""description"": ""The Land use footprint for production indicator quantifies the total land area required to produce the raw material sourced."", ""interpretation"":""The land footprint indicator describes the total area of land required to produce the quantity of a raw material sourced. It is designed to align with the Science Based Targets Network’s (SBTN) land footprint reduction target (Science Based Targets Network 2023a)."", ""license"": ""CC BY 4.0"", ""geographic coverage"": ""Global coverage."", ""citation"": [ ""International Food Policy Research Institute. 2019. 'Global Spatially-Disaggregated Crop Production Statistics Data for 2010 Version 2.0'. Harvard Dataverse. https://doi.org/10.7910/DVN/PRFF8V."" ], ""source"": [ ""Mapspam 2010"", ""LGW3 2010"" ], ""frequency of updates"": """", ""date of content"": ""2010"", ""resolution"": ""10x10 kilometers"" }" +"1994e7bf-5442-4061-ba48-6320574263ad","GHG emissions from farm management","GHGs (farm management)","GHG","The GHG emissions from farm management indicator quantifies the amount of greenhouse gas (GHG) emissions, including CO2, N2O and CH4, arising from farm-management of the raw material sourced.","a0e8110c-fbde-4c8c-ac19-f0f69078b96b","{ ""name"": ""GHG emissions from farm management"", ""short name"": ""GHGs (farm management)"", ""name code"": ""GHG"", ""indicator type"": ""farm-level"", ""impact type"": ""Climate"", ""units"": ""tCO2eq/yr"", ""description"": ""The GHG emissions from farm management indicator quantifies the amount of greenhouse gas (GHG) emissions, including CO2, N2O and CH4, arising from farm-management of the raw material sourced."", ""interpretation"": ""Estimates the emissions of greenhouse gasses (CO2, N2O and CH4 expressed in terms of CO2 equivalent global warming potential) arising from farm management practices in the production of agricultural commodities. It is intended to align with the guidance for calculating within farm gate emissions from the land sector (Greenhouse Gas Protocol 2022)."", ""license"": ""CC BY 4.0"", ""geographic coverage"": ""Global coverage."", ""citation"": [ ""Halpern, Benjamin S., Melanie Frazier, Juliette Verstaen, Paul-Eric Rayner, Gage Clawson, Julia L. Blanchard, Richard S. Cottrell, et al. 2022. ‘The Environmental Footprint of Global Food Production’. Nature Sustainability 5 (12): 1027–39. https://doi.org/10.1038/s41893-022-00965-x."" ], ""source"": [ ""Halpern et al 2021"" ], ""frequency of updates"": """", ""date of content"": ""2017"", ""resolution"": ""10x10 Kilimeters"" }" +"d5f945c9-8636-45a2-a7c9-67a1dc8e687a","Freshwater nutrient load assimilation volume","Nutrient load","WQ","The freshwater nutrient load assimilation volume indicator estimates the annual average water volume required to assimilate the nutrient load added by the raw material sourced.","9c0da38a-6371-4c79-879b-218fc39c4700","{ ""name"": ""Freshwater nutrient load assimilation volume"", ""short name"": ""Nutrient load"", ""name code"": ""WQ"", ""indicator type"": ""farm-level"", ""impact type"": ""Water quality"", ""units"": ""Mm3/yr"", ""description"": ""The freshwater nutrient load assimilation volume indicator estimates the annual average water volume required to assimilate the nutrient load added by the raw material sourced."", ""interpretation"":""The nutrient load indicator describes the average volume of freshwater required to absorb the nutrient load created by production of the raw material. It is intended to align with the Science Based Targets Network (SBTN) water quality target indicator (Science Based Targets Network 2023b)."", ""license"": ""CC BY 4.0"", ""geographic coverage"": ""Global coverage."", ""citation"": [ ""Mekonnen, M.M. & Hoekstra, A.Y. (2011) The green, blue and grey water footprint of crops and derived crop products, Hydrology and Earth System Sciences, 15(5): 1577-1600."", ""Mekonnen, M.M. & Hoekstra, A.Y. (2012) A global assessment of the water footprint of farm animal products, Ecosystems, 15(3): 401–415."" ], ""source"": [ ""Mekonnen 2011"", ""Mekonnen 2012"" ], ""frequency of updates"": """", ""date of content"": ""1996-2005"", ""resolution"": """" }" +"a39394be-ad57-41bc-9c2c-be0949ec6193","Excess freshwater nutrient load assimilation volume","Excess nutrient load","NAC","The excess freshwater nutrient load assimilation volume indicator aims to quantify the volume by which nutrient load associated with the raw material sourced must be decreased to achieve the desired instream nutrient concentration.","9c0da38a-6371-4c79-879b-218fc39c4700","{ ""name"": ""Excess freshwater nutrient load assimilation volume"", ""short name"": ""Excess nutrient load"", ""name code"": ""NAC"", ""indicator type"": ""farm-level"", ""impact type"": ""Water quality"", ""units"": ""Mm3/yr"", ""description"": ""The excess freshwater nutrient load assimilation volume indicator aims to quantify the volume by which nutrient load associated with the raw material sourced must be decreased to achieve the desired instream nutrient concentration."", ""interpretation"": ""The excess nutrient load indicator describes the extent to which nutrient loads must be reduced to meet the desired nutrient concentration following the Science Based Targets Network (SBTN) water quality target indicator approach (Science Based Targets Network 2023b). The reduction is measured as a proportion of the total nutrient load indicator and expressed in terms of the volume of freshwater required to absorb the excess pollutants."", ""license"": ""CC BY 4.0"", ""geographic coverage"": ""Global coverage."", ""citation"": [ ""McDowell, R. W., A. Noble, P. Pletnyakov, B. E. Haggard and L. M. Mosley, 2020. Global Mapping of Freshwater Nutrient Enrichment and Periphyton Growth Potential. Scientific Reports.https://doi.org/10.1038/s41598-020-60279-w."", ""Benjamin S. Halpern, Melanie Frazier, Juliette Verstaen, Paul-Eric Rayner, Gage Clawson, Julia L. Blanchard, Richard S. Cottrell, Halley E. Froehlich, Jessica A. Gephart, Nis S. Jacobsen, Caitlin D. Kuempel, Peter B. McIntyre, Marc Metian, Daniel Moran, Kirsty L. Nash, Johannes Többen, David R. Williams. (2021) The environmental footprint of global food production.Scientific Reports.https://doi.org/10.1038/s41893-022-00965-x"",""Mekonnen, M.M. & Hoekstra, A.Y. (2011) The green, blue and grey water footprint of crops and derived crop products, Hydrology and Earth System Sciences, 15(5): 1577-1600."", ""Mekonnen, M.M. & Hoekstra, A.Y. (2012) A global assessment of the water footprint of farm animal products, Ecosystems, 15(3): 401–415."" ], ""source"": [ ""McDowell et al 2020"", ""Mekonnen 2011"", ""Mekonnen 2012"" ], ""frequency of updates"": """", ""date of content"": """", ""resolution"": """" }" +"5c133ba4-da24-46db-9c6c-ece7520f01b0","Cropland expansion in natural ecosystems","Net cropland expansion","NECR","The annual average area of cropland expansion into natural ecosystems occuring within a 50km radius attributable to the raw material sourced.","6970f9b8-eba0-4fee-b6ee-2723ce6604d4","{ ""name"": ""Cropland expansion in natural ecosystems"", ""short name"": ""Net cropland expansion"", ""name code"": ""NECR"", ""indicator type"": ""landscape-level"", ""impact type"": ""Natural ecosystem conversion"", ""units"": ""ha/yr"", ""description"": ""The cropland expansion in natural ecosystems indicator quantifies the annual average area of cropland expansion into natural ecosystems occuring within a 50km radius attributable to the raw material sourced."", ""interpretation"": ""An estimate of the annual net area of cropland expansion into natural ecosystems since 2020 within a 50km radius that is attributable to the quantity of raw material sourced using a statistical land use change (sLUC) approach. This indicator assumes that land conversion is driven by demand for land in the local area and is a conservative estimate of the amount of natural ecosystems that are lost to cropland expansion in the local area. It is intended to assist companies in prioritizing sourcing areas in alignment with Zero Deforestation and Zero Land Conversion commitments, such as the Accountability Framework Initiative (AFI) and the Science Based Targets Network (SBTN) Zero Natural Land Conversion target."", ""license"": ""CC BY 4.0"", ""geographic coverage"": ""Global coverage."", ""citation"": [ ""Mazur, Elise, Michelle Sims, Elizabeth Goldman, Martina Schneider, Fred Stolle, Marco Daldoss Pirri, and Craig Beatty. 2023. ‘SBTN Natural Lands Map: Technical Documentation’. SBTN. https://sciencebasedtargetsnetwork.org/wp-content/uploads/2023/05/Technical-Guidance-2023-Step3-Land-v0.3-Natural-Lands-Map.pdf."", ""Karra, Krishna, Caitlin Kontgis, Zoe Statman-Weil, Joseph C. Mazzariello, Mark Mathis, and Steven P. Brumby. 2021. ‘Global Land Use / Land Cover with Sentinel 2 and Deep Learning’. In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 4704–7. Brussels, Belgium: IEEE. https://doi.org/10.1109/IGARSS47720.2021.9553499."" ], ""source"": [ ""Esri/Sentinel-2 10m land use/land cover"", ""SBTN/WRI/Systemiq/WWF"" ], ""frequency of updates"": ""Annual"", ""date of content"": ""2023"", ""resolution"": ""100x100 meters"" }"